# -*- coding: utf-8 -*-

''' Autor: Neuza Figueira - n.º 6036 '''

from random import uniform
from time import clock
import matplotlib.pyplot as plt


class BubbleSort():
        ''' Class to handle Bubblesort implementation and testing.'''
        
        def bubblesort(self, A):
                '''Python Implementation of Bubblesort.'''
                '''@param A -> List to sort.'''
                for i in range(0, len(A) - 1):
                        for j in range(len(A) - 1, i, - 1):
                            if A[j] < A[j - 1]:
                                A[j], A[j - 1] = A[j - 1], A[j]

                return A


        
        def testBubbleSort(self):
                '''Testing the complexity of Bubblesort.'''
                Z = range(50, 1050, 50)
                T = []
                M = 50
                for n in Z:
                        A = [ uniform(0.0, 1.0) for k in xrange(n)]
                        tempos = []
                        for k in range(M):
                                t1 = clock()
                                self.bubblesort(A)
                                t2 = clock()
                                tempos.append(t2-t1)
                        media = reduce(lambda x, y: x + y, tempos) / len(tempos)
                        var = reduce(lambda x, y: x + (y-media)**2, [0] + tempos) / len(tempos)

                        T.append((n,media, var))

                X = [n for n, media, var in T]
                Y = [media for n, media, var in T]
                ct = 12e6
                Z = [(n**2 / ct) for n, media, var in T]


                plt.grid(True)
                plt.ylabel(u'T(n) - tempo de execução médio em segundos')
                plt.xlabel(u'n - número de elementos')
                plt.plot(X,Y,'rs', label="resultado experimental")
                plt.plot(X, Z, 'b^', label=u"previsão teórica")
                plt.legend()
                plt.show()
                pass


